AI training method helps robots carry lab-learned skills into real-world tasks
Robots are trained for specific tasks, such as cutting, using simulation. However, collecting real-world data is expensive, slow, and sometimes unsafe, particularly for tasks involving physical interaction. A new AI-based method co-developed by Aston University’s Dr. Alireza Rastegarpanah could revolutionize the way advanced robotic systems are trained for real-life tasks, making them more practical and reliable.
A collaboration between NVIDIA and academic researchers is prepping robots for surgery. ORBIT-Surgical — developed by researchers from the University of Toronto, UC Berkeley, ETH Zurich, Georgia Tech and NVIDIA — is a simulation framework to train robots that could augment the skills of surgical teams while reducing surgeons’ cognitive…
To tackle different real-world tasks, robots should be able to handle and manipulate a variety of objects and materials, including paper. While roboticists have successfully improved the ability of humanoid robots or robotic grippers to handle several materials, paper folding remains a rarely explored topic within the robotics community.
Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed…